In a wonderful and very interesting turn of events, ethical algorithms are suddenly all the rage. Cathy O’Neil wrote a book called Weapons of Math Destruction, in which she went through a couple of interesting case examples of how algorithms can work in an unethical and destructive fashion. Her examples came from the US, but that the phenomenon doesn’t limit itself on the other side of the pond.

In fact, just a month ago, the Economist reported on the rise of credit cards in China. The consumption habits in China are becoming closer to resembling Western ones, including the use of credit cards. And where you have credit cards, you also have credit checks. But how do you show your creditworthiness, if you haven’t had credit?

Enter Sesame Credit, a rating firm. According to the Economist, they rely on “users’ online-shopping habits to calculate their credit scores. Li Yingyun, a director, told Caixin, a magazine, that someone playing video games for ten hours a day might be rated a bad risk; a frequent buyer of nappies would be thought more responsible.” Another firm called China Rapid Finance relies on looking at users’ social connections and payments. My guess would be that their model predicts your behavior based on the behavior of your contacts. So if you happen to be connected to a lot of careless spend-a-holics, too bad for you.​Without even getting to the privacy aspects of such models, one concerning aspect – and this is the main thrust of O’Neil’s book – is that these kinds of models can discriminate heavily based completely on aggregate behavior. For example, if CRF:s model sees your friends spending and not paying their bills, they might classify you as a credit risk, and not give you a credit card. And if there is little individual data about you, this kind of aggregate data can form the justification of the whole decision. Needless to say, it’s quite unfair that you can be denied credit – even when you’re doing everything right – just because of your friends’ behavior.

Four credit ratings, coming down hard.

Now, O’Neil’s book is full of similar cases. To be honest, the idea is quite straightforward. The typical signs of an unethical model (in O’Neil’s terms, a Weapon of Math Destruction) has a few signs: 1) they have little to no feedback to learn from, and 2) they make decisions based on aggregate data. The second one was already mentioned, but the first one seems even more damning.

A good example of the first kind is generously provided by US education systems. Now, in the US, rankings of schools are all the rage. Such rankings are defined in with a complicated equation, that takes into account how well outgoing students do. And of course, the rankings drive the better students to the better schools. However, the model never actually learns any of the variables and their importance from data – these are all defined by pulling them from the administrators’, programmers’, and politicians’ collective hats. What could go wrong? What happens with systems like these, is that the ranking becomes a self-fulfilling prophecy, and that changing how the ranking is calculated becomes impossible, because the schools that do well are obviously up in arms about any changes.

This whole topic of discrimination in algorithms is actually gaining some good traction. In fact, people at Google are taking notice. In a paper that was recently presented at NIPS, the authors argue that what is needed is a concept of equality of opportunity in supervised learning. The idea is simple: if you have two groups, (like two races, or rich and poor, etc.) in both groups the true positive rate should be the same. In the context of loans, for example, this means that of all those who could pay back loans, the same percentage of people are given a loan. So if groups A and B have 800 and 100 people that could pay the loan back, and your budget can account a loan to 100 people, then 88 in group A and 11 in group B would get the loan offer (both having 11% loan offer rate).​Mind you, this isn’t the only possible or useful concept for reducing discrimination. Other useful ones group-unaware and demographic parity. A group-unaware algorithm discards the group variable, and uses the same threshold for both groups. But for loans, depending on the group distributions, this might lead to one group getting less loan offers. A demographic parity algorithm, on the other hand, looks at how many loans each group gets. In the case of loans, this would be quite silly, but the concept might be more useful when allocating representatives for groups, because you might want each group to have the same number of representatives, for example.Anyway, there’s a really neat interactive graphic about these, I recommend you to check it out. You can find it here.

When it comes to cultural norms, there’s a sentiment that those norms are right. This makes sense, because otherwise they wouldn’t be norms, would they. Now, most people recognize that norms are different between different groups of people. Stereotypically, I might believe that in Latin America it’s quite ok to be late (free tip: don’t turn up at the time when the Facebook event starts). In contrast, here in Finland, if you show up late you will be left out. I mean really, left out in the cold, with the door shut in your face.​The same understanding of differences – if not acceptance – goes for subgroups within nations. I’m part of the environment-loving, mollycoddling group of so-called experts, who would like to “protect” the environment by reducing car travel (ie. wrecking the lives of county-folk), reduce crime by rehabilitation (too soft on crime), and think that policy needs careful analysis (meaning apparently I know better than the people). Some other people are – from my bubble perspective – country-loving idiots who just want to have the state pay for their lifestyle, even though obviously we can’t have a university hospital in every village. Now, everybody is wrong here, and that’s fine. Just one beautiful part of being a human: identifying with your tribe and mischaracterizing the others.

However, what really is interesting is this: we are bad at recognizing the change in the norms of our society. Standards can change, and they will. They will change in ways that you won’t foresee or expect. To take an example, let’s go back to the year 1937.

In 1937, Sylvan Goldman tried to get people in his supermarket to use his new invention - shopping carts. He thought having carts on wheels would make shopping a lot easier. However, customers rejected the new carts, and didn’t want to use them. Why? Because the carts were deemed to be unmasculine. What? But how is shopping related to masculinity?

It isn’t – not anymore. However, at that time, it was generally believed that a true man can carry the groceries for his family. By implication, using a cart means that you’re not a proper man. If other people saw you using that one – well, you see them watching you with a look saying “I always knew what a wimp you are, Smith – you can’t even carry your groceries!”. (Drifting corners with the cart probably doesn’t help here.) Also, women didn’t want to use them either, because apparently the carts reminded them too much of baby strollers.

When I read about this, I felt like I’m reading a science fiction book. The norm of masculinity that disallows a shopping cart is just…unrecognizable. And it’s not that far away in terms of years. But just somehow, we’ve transitioned from the shopping-carts-are-unmanly norm to a time where pushing shopping carts (or even strollers!) is ok for men, even for fun. Who would’ve known? (Well, Goldman did and made millions out of it).

Anyway, next time you find yourself thinking “well, that’s never going to be acceptable”, remember shopping carts.

In the world of busy worker bees, there’s only one specimen that can do all and any tasks necessary: the scientist. While other lower animals can often be found in concerted work, where opportunity costs and benefits of specialization define who does what, the scientist is well above the need for help. Preferring solitude to socialization and caffeine to cooperation, the solitary scientist is an image of good old days, when society was driven forward by heroic explorers and experimenters.​How does the scientist retain this mastery over all matters? The answer is a combination of two issues. First of all, due to the solitary nature of the scientist, each one prefers to work on issues on their own. Trying to cooperate would result in loss of status, because the other individual might be better at something (gasp!). After all, isn’t image above all? Better to do your web app for the experiment from scratch, rather than enlist the help of a computer scientist. On another level, the inclusion of additional people would mean a larger group for dividing spoils. You see, publishing a paper just by yourself in The New England Hyperprogressive Journal of Foucauldian Energy Fields is surely better than one in Nature, if in the latter you have to share the spoils with other people. Working alone, you can be the heroic explorer of your dreams. Working together, you’re just a cog in a machine, and nobody will remember your name. Especially if that other one is the first author.

A scientist making a display of his fitness to a competitor.

This way of noncooperation manifests itself also in the physical structure of scientist life. Whereas other species tend to have open-plan dwellings that promote interaction, scientists’ lairs are typically made of single-person rooms. This ensures that each individual can stay nonproductive at their own pace, and means that interaction tends to happen only when the scientists gather around the local coffee pond to drink. Even that isn’t universal: in many societies, scientists have long learned that they can avoid these semi-forced interactions by having their own source of nourishment – a small espresso machine. This is a great way to show that you are not dependent on the tribe for anything. In contrast, if you are forced to accept other scientists in your territory, it shows that you have not earned your stripes to obtain the honorary title of a doctor. Or if you have, then sharing territory is a sign of weakness, both of your research and yourself.​As the night appears, almost nothing changes. Since in daytime everyone would be sitting behind closed doors anyway, just by observation you can’t tell it’s already late. The disappearance of administrative personnel, however, signals the end of the hottest time of day. But if you could see behind those closed doors, you could find many a scientist, still procrastinating profusely. It’s a world of publish or perish, and there’s this critical deadline that you are close to missing (because you just spent two months learning how to do PHP, instead of that conference paper).

If you’re even slightly familiar with the last decade’s deluge of pop science books in psychology, you probably have heard of the phenomenon that sudden events and life events like marriage, death of a spouse, winning the lottery, etc. don’t matter that much for happiness. Instead, you have a set point that you bounce back to in a couple of years, if not even faster. This has been quoted at least in Gilbert’s Stumbling on Happiness, and in Kahneman’s Thinking Fast and Slow. Here’s a typical pattern:

Source: Kahneman & Krueger (2006). Developments in the Measurement of Subjective Well-Being. Journal of Economic Perspectives, 20(1), pp. 3-24.​They all claim the same thing: even if you become a quadriplegic – or win the lottery – this has no impact on your happiness in the long term. In a few months or years, you’ll adapt and be right back to your happiness set point.

However, it’s not like that.​A meta-analysis from 2012 Luhmann, Hofmann, Eid & Lucas in the Journal of Personality and Social Psychology goes through a swathe of research about the impact of life events. Crucially, they look at longitudinal studies, which in this case are much better than just cross-sectional designs. Anyway, technicalities aside, let’s dive right into the main findings.Here’s a picture from the paper:

Here, the event (childbirth) happens at time t=0. The points are effect sizes, which compare the difference in emotional or affective well-being (AWB) to the value at the event. The CWB; LS and CWB: RS reflect to effect sizes for life satisfaction and relationship satisfaction. In the middle, the black straight line is the estimated level of AWB before the event, while the dashed straight line is the same, but for life satisfaction. The curves are just log model estimates for how the effect sizes for AWB, RS and LS develop over time.

The crucial point is that, even after 100 months (9 years!) the life satisfaction still hasn’t returned to the baseline. Since the estimated level before the event is negative, we know that LS is typically lower before the event than at t=0. This makes sense, since having a child is an exciting experience, and creates a good sense of achievement for many. For the life satisfaction to reach that baseline of before anticipating a child, it would have to reach the dashed straight line. This would then mean it’s that much lower than at childbirth, ie. the same as before anticipation.​Similar graphs can be found for marriage, divorce, losing one’s job, and rehiring. For example, the marriage one looks pretty similar:

​Once again, the story is similar. Even after 10 years, life satisfaction is still not at the baseline, but slightly above the EPL. Emotional well-being seems to be unaffected by marriage, though there are only five estimated effect sizes.

For me, this is shocking. The adaptation hypothesis seemed to fit in together with everything I had read about happiness. (Of course it did, since all books referenced the same phenomenon.) Now, by golly, it looks like losing your partner does in fact make you unhappier. Even if this might be “common sense”, it’s prudent to remember that the same meme has been all over the place. Even in books and talks that have appeared after the meta-analysis.

If you’re a research psychologist who specializes in happiness, this is probably no news. However, if you’re anybody else, chances are that the happiness adaptation meme has found its way to your mind and entrenched itself deep. It certainly did that for me. I mean, you keep seeing the same thing in every book, so it must be true! But like so many memes, this one if false too.

What are the implications? Well, for me, this definitely decreases my confidence on the whole in the happiness set point hypothesis. I used to think that the set point was probably generated through some interaction of genetics, early life experiences and social environment. I used to think that it was very robust to changes in your personal wealth, job situation etc. Now, I’m not so sure. It could still be that the set point hypothesis still holds. Maybe the set point is just not as robust as I used to think.

However, what the meta-analysis seems to imply is that the set point itself can be changed. If the life events can impact your set point over the course of several years, it makes more sense to talk about change in the set point, instead of lags of several years.​P.S. On a tangent, finding out about this article was interesting validation for reading outside my own field (commonly called procrastinating). Wellbeing psychology is generally interesting, but I’m definitely not an expert on it. If I tried to read the journals in the field, I’d never get any work done – and would suffocate myself with what are (to me) irrelevant papers. There’s just too much stuff. But how do you separate the wheat from the chaff? Blogs can help: this gold nugget came through a psych/science blog, which had mentioned the finding (thanks, Scott). Call me out on the N=1 if you want, but now I feel again that this blog-reading is useful (and not just pointless PhD procrastination).

If you read almost any book about ”productivity”, ”efficiency” or similar, one of the tips you’re bound to get is to have fewer meetings. In my job, I’d say I have a meeting on average twice a month. So I must be the happiest, most efficient worker in the history of the universe, right? Not exactly.

I’ve found out that I love meetings. Not because they are often basically a group of people sitting around, drinking coffee, and pretending that the discussion totally justifies spending 15 minutes on the current state of politics, sports, or playing didyouseethatarticleaboutthethingilove. Or how many meetings are just rehashes of the old ones, because people a) don’t remember what you talked about last time, and b) haven’t done what was agreed on previously. So you pretty much keep going through the same points again (well, at least you won’t need new slides).

All this aside, I’ve found that meetings have an important motivational effect on me. Working from Germany for projects in Finland means that on a lot of days, I’m basically sitting alone in my yuge office (well, huge for a PhD student). We do have lunches together with colleagues at the office – but otherwise the social interaction is very academic: everyone says “hi” in the morning, and then retreats to their own cave…I mean room for the day. So, for most of the time it’s just me and the computer. Actually, it’s me and two computers, since I’m carrying a laptop to work in the train. Fantastic, twice the capacity for procrastinating online!

Oh yeah, the meetings. It may sound silly or obvious, but I see now that it’s way much easier to work, when you get to have a meeting every once in a while, talk to your supervisor about your work, and get at least a fleeting feeling that someone actually cares about the project you’re working on. I do realise that research is very much driven by intrinsic motivation – most people do the PhD because they’re really curious about some topic, not because they want to get a better wage or impress someone else (btw, these reasons seem way more common in Germany). But the fact that someone else is also invested in the project totally sparks me. I don’t know why, but so far I get the feeling that it’s related to a sense that I’m not willing to let other people down. So, at least for me, there definitely seems to be a floor level of meetings that I should have, because it helps to keep up my motivation. No book has ever mentioned this effect, but I guess most workplaces are chock full of meetings, so that this is not a relevant risk – unlike coffee and bun overdoses.​With these thoughts in mind, I can honestly say I’m really looking forward to flying to Helsinki tomorrow, and attending some meetings! Another point to improve motivation would be to somehow make me seem more connected to our work group when here, but so far I’m lost on how to do that. If anyone else has any good tips from their teleworking experiences, help is appreciated!

​The past two months, I’ve been completing University of Michigan’s fantastic Model Thinking course, available for free on Coursera. There’s so much to love about the modern world: you can learn interesting things through quality teaching, no matter where you are (well, you need a wifi), no matter when. And it doesn’t cost a cent!

Anyway, the course had a section about Random Walks, and it got me thinking. A while back I wrote about how the nonlinear life and our linear emotions aren’t exactly optimally suited to each other. Your brain craves signs of progress, so it could reward you with a burst of feel-good chemicals. Unfortunately, the nonlinear life doesn’t work like that. Often, you can spend days or weeks slaving away at the office/studio/whatever, not really moving forward – or even taking two steps back for each move forward. Despite the hours that you put in, the article/thesis/design never seems to be finished, making you question whether you’re really cut out for this kind of job. Perhaps you’d do the world a favor by setting your sights lower and working as a sales clerk instead.

Now, while watching one of the course lectures, I suddenly realized that the creative nonlinear work is exactly a random walk! I don’t claim this to be a unique insight or anything – I’m sure many of you have realized this before. But for the fun of it, it might be a nice exercise to show with a random walk model how the nonlinear life functions. At least in my own case, models often help to see the bigger picture, and forget about the noise in the short term. And who knows, maybe this will help to quell those linear emotions, too.

So, a random walk is very simple. In this case, let’s assume that we have a project that has a goal we’re trying to reach. Arbitrarily, let’s say that the completion means we reach a threshold of 100 points. Of course, these numbers are completely make-believe and I pulled them from my magical hat. Now, further, let’s assume that each unit of time – say 1 unit equals 1 day – means we have three possibilities: make progress, stay where we are, or take steps backward. In my personal experience, this is an ok model for work: sometimes you’re actually making progress, and things move smoothly. Sometimes, though, you’re actually hurting your project, for example by programming bugs into the software, which need to be fixed later on (just happened to me two weeks ago). Most often, though, you’re trying your best, but nothing seems to work. Maybe you’re stuck in a dead end with your idea, and need to change tack. Maybe you’re burdened with silly tasks that have nothing to do with the project. Well, I’m sure we all have these kinds of days.So let’s again use my magical hat and pull out some probabilities for these options. Let’s say you have a 5% chance of making a great jump forwards (10 points), 25% chance of making 3 points of progress, 55% chance of getting stuck (0 points), 10% chance of making a mistake (-2 points), and a 5% chance of doing serious damage (-6 points). Now we just simulate these across and get a graph that shows your cumulative progress towards the goal (yes I'm doing this in Excel):

​So, in the graph there are several periods when it’s just going downhill, or plateauing for several time periods. Even though the numbers are really made up, I feel the above graph is actually a pretty decent example of how the nonlinear work often feels. However, there’s still the additional complication: the emotions.

Suppose that our emotions work as follows. If you’re making progress, you feel good. And this is mostly irrespective of how much progress you’re making. Suppose the same holds for drawbacks – it hurts, but it hurts almost as much to look for a bug for two hours or the full day. Finally, I’ll assume that if you’re not moving anywhere, you inherit the feeling from the day before. Now, I realize this is probably not how emotions really work (we’re often annoyed by our administrative duties, for example). But on the other hand, when I have a day I have spent at a dull seminar, I seem to find myself looking back a bit to evaluate the progress. The “inherit from t-1” rule tries to describe this: I feel good if the past has been good, and I feel annoyed if the past wasn’t successful. Why just t-1 and not the actual level? Well, I’ve also found that it’s really hard to evaluate how far the project actually is, which makes that option unrealistic. And when looking back, our memories are much stronger from the immediate past than the long-gone part. In short, I’m modeling here the short-sightedness. The actual progress-emotions payoff table looks like this:

So with these assumptions, we get the following graph portraying emotions:

Now this is pretty interesting! You can see how 1) there’s a lot of fluctuations back and forth, and 2) how there’s still “runs”, ie. the same emotional state tends to linger for a while. If you run the numbers, with this particular string of successes and failures you get 99 positive time periods and 51 negative ones, out of the total 150 periods I ran the simulation for. I think the above graph is quite a good summary of how the nonlinear life often feels: you love you’re job, but you’re not above hating it when things are not going well.

A final word of warning: this was of course just one simulated outcome. With the exact same parameters, you can get project outcomes that never finish, that run into negative progress, that finish in less than 30 periods, etc. They are not very nice for terms of a presentation, but also capture the great amount of uncertainty in a nonlinear project. Sometimes it just falls apart, and after 50 periods you’re back to exactly where you started. Or that a project you thought takes 6 weeks takes 16 weeks instead. Well, I’m sure everyone has had these experiences.

I’m currently staying through Airbnb with a lovely couple in Strasbourg at the moment. They were yesterday out, watching a stand-up comedy show, which apparently is done by some very famous actors, and you have to wait for tickets forever. Unfortunately, the comedy turned to tragedy as the audience was after the show told what had happened in Paris. I read the news this morning, and couldn’t get any more sleep. What a shock.

Last night, terrorists struck in Paris, killing at least 120 people, and injuring a hundred more. The attacks seemed very professional, striking six targets including restaurants, a concert hall, and a football stadium. If you want more details, any news site will have good coverage.This feels surreal: perhaps the most secular country in Europe suffers its worst terror attack since WWII, most likely perpetrated by Islamist extremists. And this happened so soon: it was only in January, when terrorists struck against the satirical magazine Charlie Hebdo. In June, there was the attack in Grenoble. What is concerning is the strain on the French public and morale that these attacks are going to have.

This was the most massive attack in the European West for decades that has struck against targets of the general public. Fair enough, the targets this time were mostly inside Paris’ multicultural districts – in the center, the death toll could have been so much worse. But the move from targeting a newspaper to killing ordinary citizens selected at random, makes for a completely different situation. For sure, the effect on the feeling of security is much heavier.

What worries me is the backlash after these attacks. These attacks are making people angry and scared – and with reason. However, I’m just hoping that the response to these attacks is not going to be a war against Islam, but a war against extremism. In fact, after the Charlie Hebdo attacks, Prime Minister Valls made similar comments, saying that France is at war against radical Islamism, extremism, and terrorism – but not against a religion. I also hope France will not turn to the far right parties for guidance, since their response is likely going to be a version of “close the borders, and kick out all muslims”, just with more obscure political jargon.

I think the best response the French people can do is stick to their values – keep France a secular state, keep going out to cafés, keep living their lives. Because ultimately, terrorism is just a way of inciting hatred. If hatred is created against Islam in general, even more muslims are going to feel left out, and then turn radical. That’s hardly going to be a recipe for success. Better to say: “hey, I don’t care in which god you believe in, just follow the democratic laws we have”. To be clear: the problem of Islamist extremists and terrorism is not the Islam, it’ the terrorism. No matter what you religion, killing others is a dick move.

I hope France doesn’t fight hatred with more hatred. I hope it stays true to its morals, and is the “bigger person” here. More security measures at home won’t solve the problem. Terrorism is only successful when it creates fear and hatred – we can all prove the terrorists wrong by not playing that game, by showing we’re not scared. Like, for example, by singing the national anthem when you’re being evacuated. Of course, police and state security have to try to stop all the strikes they can. But, barring a police state, that isn’t going to happen. In the end, terrorism ends only when the will of terrorists to blow themselves up ends.​So, I hope France remains France. Liberté, égalité, fraternité. Viva la France!

The past month or so I’ve been reading Taleb’s Black Swan again, now for the second time. I’m very much impressed by his ideas, and the forceful in-your-face way that he writes. It’s certainly not a surprise that the book has captivated the minds of traders, businesspeople and other practitioners. The book is extremely good, even good enough to recommend it as a decision making resource. Taleb finds a cluster of biases (or more exactly, puts together research from other people to paint the picture), producing a sobering image of just how pervasive our neglect of Black Swans is in our society. And, he’s a hilariously funny writer to boot.

But.

Unfortunately, Taleb – like everyone else – succumbs in the same trap we all do. He’s very adept at poking other people about their biases, but he completely misses some blind spots of his own. Now, this is not evident in the Black Swan itself – the book is very well conceptualized and a rare gem in the clarity of what it is as a book and what it isn’t. The problem only becomes apparent in the following, monstrous volume Antifragile. When reading that one a few years ago, I remember being appalled – no, even outraged – by Taleb’s lack of critical thought towards his own framework. In the book, one gets the feeling that the barbell strategy is everywhere, and explains everything from financial stability to nutrition to child education. For example, he says:

​I am personally completely paranoid about certain risks, then very aggressive with others. The rules are: no smoking, no sugar (particularly fructose), no motorcycles, no bicycles in town [--]. Outside of these I can take all manner of professional and personal risks, particularly those in which there is no risk of terminal injury. (p. 278)

I don’t know about you, but I really find it hard to derive “no biking” from the barbell strategy.

​Ok, back to seeking out irrationality. Taleb certainly does recognize that ideas can have positive and negative effects. Regarding maths, at a point Taleb says:

[Michael Atiyah] enumerated applications in which mathematics turned out to be useful for society and modern life [--]. Fine. But what about areas where mathematics led us to disaster (as in, say, economics or finance, where it blew up the system)? (p.454)

My instant thought when reading the above paragraph was: “well, what about the areas where Taleb’s thinking totally blows us up?”

Now the point is not to pick on Taleb personally. I really love his earlier writing. I’m just following his example, and taking a good, personified example of a train of thought going off track. He did the same in the Black Swan, for example by picking on Merton as an example of designing models based on wrong assumptions, and in a wider perspective of models-where-mathematics-steps-outside-reality. In my case, I’m using Taleb as an example of the ever present danger of critiquing other people’s irrationality, while forgetting to look out for your own.​Now, the fact that we are better at criticizing others than ourselves is not exactly new. After all, even the Bible (I would’ve never guessed I’ll be referencing that on this blog!) said: “Why do you see the speck that is in your brother’s eye, but do not notice the log that is in your own eye?”In fact, in an interview in 2011, Kahneman said something related:

I have been studying this for years and my intuitions are no better than they were. But I'm fairly good at recognising situations in which I, or somebody else, is likely to make a mistake - though I'm better when I think about other people than when I think about myself. My suggestion is that organisations are more likely than individuals to find this kind of thinking useful.

If I interpret this loosely, it seems to be saying the same thing as the Bible quote – just in reverse! Kahneman seems to think – and I definitely concur – that seeing your own mistakes is damn difficult, but seeing others’ blunders is easier. Hence, it makes sense for organizations to try to form a culture, where it’s ok to say that someone has a flaw in their thinking. Have a culture that prevents you explaining absolutely everything with your pet theory.

Okay, so I promised to reveal my own results for the last week’s Rationality test, and also take a deeper look at the questions while I’m at it. So here goes.

You might guess that – based on the fact that rationality is a big theme of this blog – I would receive the score “Rationalist”. Well, you’d be half right. When I was trying out the beta version of the test (with slightly different questions I think), I got “Skeptic”. Also, the more rationalist result of the second try was a lot due to the fact that for some questions I knew what I was supposed to answer. I guess this shows there’s still room for improvement. Anyway, so what are you supposed to answer, and why? I’ll go through the test question-by-question, providing short explanations and links to relevant themes or theories. At the end, I’ll show how the questions relate to the skills and sub-categories.

Question-by-question analysis1. LA movieWell, this is just a classic case of sunk costs. You’ve invested time and money into something, but it’s not bringing the utility (or profit, or whatever) that you thought. If you abandon it, you abandon all chance of getting any utility out from the investment. However, as far as rationality is concerned, past costs are not relevant, since you can’t affect them anymore. The only thing that matters is the opportunity cost: should you stay in the movie, or rather do something else. If that something else brings more benefits, rationally you should be doing that.

2. Music or jobThis problem is a classic case of how to confuse your opponents in debates. You can see this in politics, like suppose you’re talking about school shootings with a hard-line conservative: “Either we give guns to teachers or we lower the right to bear arms to 8 years old!”. Well, of course you see right away that you’re being presented a false dichotomy: there are many other ways to prevent shootings – like banning all arms altogether. But to skew the debate and try to put you in a position in which you have to accept something you don’t like, your opponent tries to lure you into the these-are-the-only-two-options trap.

3. Doughnut-making machineNow, this question is basically just simple arithmetic. However, the trick here is that the answer that immediately comes to mind is incorrect, ie. a false System 1 response. Instead, what you need to do is to mentally check that number, see it is wrong, and use System 2 to provide the right answer. The question itself is just a rephrase of one question in the classic Cognitive Reflection Test.

4.Fixating on improbable frightening possibilitiesI’m a little puzzled about this question. Sure, I understand the point is that if you’re always fixating on really unlikely bad things, you’re doing something wrong. Still, I find it hard to see anyone would actually be like this!

5. The dead firemanNow, the point in this question was to see how many possible causes you would think of before deciding on one. The idea is, naturally enough, confirmation bias. We’re too often thinking of a certain explanation, and then immediately jumping to look for confirming evidence. In complex problems, this is a special problem since as we all know, if you torture the numbers enough with statistics, you can make them confess to anything.

6. Things take longerWell, I presume this simple self-report question is just measuring your susceptibility to the planning fallacy.

7. Bacteria dishThis question has the same idea as the Doughnut-making machine. You get a System 1 answer, suppress it, and (hopefully) answer correctly with System 2. This question is also a rephrase of a question from the Cognitive Reflection Test.

8.Refuting argumentsBeing able to argue against other people is a clear sign of rhetorical skills and logical thinking.

9.Budgeting time for the essayThis question checks the planning fallacy. Often, we’re way too optimistic about the time that it takes to complete a project. For example, I’m still writing a paper I expected to be ready for submission in May! In this question, you were awarded full points for assigning at least 3 weeks for the essay, ie. the average of the previous essay completion times.

10. Learning from the pastThis is a simple no-trick question that honestly asks whether you learn from your mistakes. I honestly answered that I often do, but sometimes I end up repeating mistakes.

11. BacoNation sales and the ad campaignThis checked your ability to use statistical reasoning. True enough, sales have risen compared to the previous month, but all in all the sales have varied enough to make it plausible that the ad campaign had no effect. In fact, if you pnorm(44.5, mean(data), sd(data)), you get 0.12167, which implies that it’s plausible the September number comes from the same normal distribution. This makes the effect of the ads only somewhat likely.

12. Sitting in the trainSo this is the first of the two questions that check how much you value your time. Of course, the point here is that you ought to be consistent. Unfortunately, there may be valid arguments for claiming that you value time on holiday and at home differently, due to differing opportunity costs. See question 20 below for more explanation.

13. Value of timeThis question simply asks whether you find it easy or difficult to value your time. Unsurprisingly, the easier you find it the higher your points.

14. One or two mealsWould you rather have one meal now or two meals in a year? This is measuring the discounting of time. Assuming that you’re not starved of food, you presumably should discount meals in the same way as money, since money can obviously buy you meals. See question 21 below for a longer explanation.

15. Continue or quitAnother one of those self-report questions, this is basically asking whether you have fallen into the sunk cost trap.

16. 45 or 90Here’s another question about time discounting, this time with money. The same assumptions hold as before: we’re assuming you are not in desperate need of money. If that holds, you should discount the same way over all time ranges.

17. Certainty of theoryCan a theory be certain? If you’re a Bayesian (and why wouldn’t you be, right?), you can never set a theory to be 100% certain (let’s ignore tautologies and contradictions here). In a Bayesian framework this would mean that no matter what evidence you observe, the theory can never be proven wrong, because a prior of 1 discounts any evidence for or against it.

18. 100 vs 200Another discounting question, this time with slightly different amounts of money. Once again, you should discount the same way and choose whatever you chose before. Note that here we are also assuming that 100/200 amounts are close enough to the 40/90 decision – if we had amounts in the millions, that might make a lot of impact.

20. Paying for a taskThis question is a sister question to the one where you’re sitting in the train. I presume that the point is that your valuation of one hour should be the same in both question. However, we can question whether the situations are really the same. In one, you’re one holiday, and sitting in a train in a new city has positive value for me. What’s more, on holiday the opportunity costs are different. I’m not really trading off time for working hours, because the point of the holiday mindset is precisely setting aside the possibility of work, so I can enjoy whatever I’m doing – like sitting in a train in a new city. In this question, you’re trying to avoid a task at home, where the opportunity costs of one hour may certainly be different than when you’re on holiday. For example, if you have a job you can do from home, you could be working, or going out with friends, etc.

21. 45 or 90Well, this is of course part of the other time discounting questions. Here we have the same 45/90 amounts, but the time has been shifted for one year to the future. Again, you should choose whatever you chose before.

All these questions had the similar format:A dollars in time t vs. B dollars in time t+T

If you’re perfectly rational, you should discount in the same way between times [now, 3 months] and [1 year, 1 year 3 months]. The reason is quite simple: if you’re now willing to wait for the extra three months but not when the lower amount is immediate, you will in the future end up changing your decision. And, if you already know you will change it, why wouldn’t you choose that option already. Hence, you should be consistent. (if you really need an academic citation, here is a good place to start)

As a decision scholar, I’m a firm believer in the benefits of specialization. If someone is really good at doing something, then it’s often better to rely on them in that issue, and focus efforts towards where you’re personally the most beneficial to others and society at large. Of course, this principle has to apply over all agents – including myself. With that in mind, I’m going to make a feature post about something a certain someone else does – and does it much better than me.

Enter Spencer Greenberg. I’ve talked to Spencer over email a couple of times, and he’s really a great and enthusiastic guy. But that’s not the point. The point is that he does a great service to the community by producing awesome tests, which you can use to educate yourself, your partner or anyone you come across about good decision making. What’s even better is that the tests are done with the right kind of mindset: they’re well backed up by actual, hard science. What this means is that the questions make sense – there’s none of that newspaper-clickbait “find your totem animal” kind of stuff. There’s proper, science-backed measuring. Even better, the tests have been written in a way anyone can understand. You don’t need to be a book-loving nerdy scholar to gain some insights!​Now, I’ve always wanted to bring something to the world community. And a while ago, I thought maybe I could produce some online tests about decision making. But after seeing these tests, I’ll just tip my hat and say that it’s been done way better than I ever could have! Congrats!And now, enough of the babbling: go here to test yourself! (For comparison, a reflection of my results can be seen in next week’s post :)